Presentation
STEM-CAM: Segmented Thermometer-Encoded Multibit CAM with Wildcard Matching for Efficient Arbitrary-Order Minkowski Distance Metrics
DescriptionMinkowski distance metrics (Lₚ) are widely used in AI. For hardware, digital designs incur limited parallelism and high energy/latency. Alternatively, content addressable memory (CAM) supports large-scale parallel distance metrics. However, existing CAM-based designs mainly support L₀/L₁, with limited scalability/configurability. In this work, we propose a segmented thermometer-encoded multibit CAM (STEM-CAM) architecture, enabling arbitrary-order Lₚ metrics. Segmenting distance norms with wildcard-augmented thermometer encoding, STEM-CAM enables a structured, scalable encoding for Lₚ. Moreover, an ultra-compact FeFET-based encoding-aware multibit CAM cell is proposed, providing efficient, high-precision implementation. Experiments demonstrate high throughput and area/energy efficiency for Lₚ, showing great potential for distance-based AI.
Event Type
Research Manuscript
TimeMonday, July 2710:43am - 10:56am PDT
LocationMtg Room 202C
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